Probabilistic Modelling of Concrete Abrasion Due to Moving Sea Ice
Bibliographic record
Abstract
A probabilistic model using the method of Monte-Carlo has been developed to assess the annual abrasion due to sea ice. The model uses the laboratory model of Itoh et al. (1994), who presented the abrasion as a function of ice temperature and ice pressure. It is believed that the properties of both the ice and the concrete will affect the amount of expected abrasion, and a factor representing the quality of the concrete has been introduced, which includes the compressive strength, the water/cement (w/c) ratio, and the amount of silica fume. The effects are taken care of in a best possible manner, based on observations and findings in the available literature. The results from the Monte-Carlo simulator are checked against the measured abrasion from two different locations, the Sydostbrotten lighthouse in the Gulf of Bothnia, Sweden, (Janson, 1988), and the bridge piers at the Confederation Bridge crossing the Northumberland Strait, Canada (Newhook and McGinn, 2007). Both the concrete properties and the ice conditions are different for these two cases, and compared to these two it can be said that the model predicts the concrete abrasion measured in the field reasonably well.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".